from cmath import inf
from typing import Dict, Tuple, Optional, List

import torch
from gym import Env, spaces
from gym.spaces.dict import Dict as DictSpace
from gym.spaces.discrete import Discrete
from rl4lms.data_pools.text_generation_pool import Sample
from rl4lms.envs.text_generation.reward import BatchedRewardFunction, RewardFunction
from rl4lms.envs.text_generation.observation import Observation
from transformers import AutoTokenizer
from rl4lms.core_components.sampler import PrioritySampler

import inspect

class TextGenEnv(Env):
    def __init__(
        self,
        tokenizer: AutoTokenizer,
        reward_function: RewardFunction,
        samples: Tuple[List[Sample], float],
        max_episode_length: int = 512,
        priority_scale: float = 0.0,
        max_prompt_length: Optional[int] = None,
        terminate_on_eos: bool = False,
        context_start_token: Optional[int] = None,
        prompt_truncation_side: str = "left",
    ):
        """
        A generic RL environment to generate textual sequences.
        For eg: text generation, summarization, machine translation, text simplification
        Args:
            tokenizer (AutoTokenizer): pre-trained tokenizer
            reward_function (RewardFunction): reward functiom
            samples (Tuple[List[Sample], float]): list of samples
            max_episode_length (int, optional): Max steps to the model Defaults to 512.
            priority_scale (float, optional): weight for the priority sampler Defaults to 0.0.
            max_prompt_length (Optional[int], optional): maximum prompt length. Defaults to None.
            terminate_on_eos (bool, optional): whether to terminate on EOS. Defaults to False.
            context_start_token (bool, optional): start token for the context (For Encoder-Decoder models! )
            prompt_truncation_side (str): truncation side for prompt text (Defaults to "left")
        """
        self.tokenizer = tokenizer
        self.reward_function = reward_function
        self.max_steps = max_episode_length
        self._max_text_length = (
            max_prompt_length if max_prompt_length else tokenizer.model_max_length
        )
        self._terminate_on_eos = terminate_on_eos
        self._context_start_token = context_start_token
        self._prompt_truncation_side = prompt_truncation_side
        super().__init__()

        # set the observation and action space here
        self._vocab_size = tokenizer.vocab_size
        self.observation_space = DictSpace(
            {
                # we have to provide fixed sized inputs (padded) because sb3 support for DictObsersevation is limited
                # while creating rollout buffers, observations are concatenated for each key
                "prompt_or_input_encoded_pt": spaces.Box(
                    low=0, high=self._vocab_size, shape=(self._max_text_length,)
                ),
                "prompt_or_input_attention_mask_pt": spaces.Box(
                    low=0, high=1, shape=(self._max_text_length,)
                ),
                "context_encoded_pt": spaces.Box(
                    low=0, high=self._vocab_size, shape=(self.max_steps,)
                ),
                "context_attention_mask_pt": spaces.Box(
                    low=0, high=1, shape=(self.max_steps,)
                ),
                "input_encoded_pt": spaces.Box(
                    low=0,
                    high=self._vocab_size,
                    shape=(self._max_text_length + self.max_steps,),
                ),
                "input_attention_mask_pt": spaces.Box(
                    low=0, high=1, shape=(self._max_text_length + self.max_steps,)
                ),
            }
        )
        self.action_space = Discrete(n=self._vocab_size)
        # see https://github.com/huggingface/transformers/issues/4875 : rounding up to nearest power of 2 for better GPU efficiency
        if 'mt5' in self.tokenizer.name_or_path:
            n = 250112
            self.action_space = Discrete(n=n)
        elif 't5' in self.tokenizer.name_or_path:
            n = 32128
            self.action_space = Discrete(n=n)
        self.sampler_for_replaying = PrioritySampler(priority_scale=priority_scale)
        for sample, weight in samples:
            self.sampler_for_replaying.add(sample, weight)

        # check the tokenizer and add padding tokens
        if self.tokenizer.pad_token is None:
            self.tokenizer.pad_token = self.tokenizer.eos_token
        self.tokenizer.padding_side = "left"  # TBD: configure this
        self.tokenizer.truncation_side = "left"  # TBD: configure this

        # init tracking variables
        self.__current_sample = None
        self.__current_obs = None
        self.__time_step = None

    def step(self, action: int) -> Tuple[Dict[str, torch.tensor], int, bool, dict]:
        self.__time_step += 1

        # previous obs
        previous_obs = self.__current_obs

        # just update the context tensor and gets the new observation
        self.__current_obs = self.__current_obs.update(action, self.tokenizer)

        # decide if the episode is finished or not
        done = (action == self.tokenizer.eos_token_id and self._terminate_on_eos) or (
            self.__time_step == self.max_steps
        )

        # compute reward
        if not isinstance(self.reward_function, BatchedRewardFunction):
            reward = (
                None
                if self.reward_function is None
                else self.reward_function(
                    previous_obs,
                    action,
                    self.__current_obs,
                    done,
                    self.__current_obs.meta_info,
                )
            )
        else:
            reward = -inf  # will be overridden later


        # populate additional info
        info = {
            "output": self.__current_obs.context_text,
            "action_history": self.__current_obs.action_history,
            "reference_text": self.__current_obs.target_or_reference_texts,
            "prompt_text": self.__current_obs.prompt_or_input_text,
            "prev_output": previous_obs.context_text,
            "meta_info": previous_obs.meta_info,
        }

        return self.__current_obs.to_dict(), reward, done, info

    def reset(self, sample: Sample = None) -> Dict[str, torch.tensor]:
        """
        Resets the environment and starts a new episode
        """
        # gets a new sample if not provided
        if sample is None:
            sample = self.sampler_for_replaying.sample(size=1)[0]
        self.__current_sample = sample

        # init the observation
        self.__current_obs = Observation.init_from_sample(
            sample,
            self.tokenizer,
            self._max_text_length,
            self.max_steps,
            self._prompt_truncation_side,
            self._context_start_token,
            sample.meta_data,
        )

        # start the time step counter
        self.__time_step = 0

        dict_observation = self.__current_obs.to_dict()
        return dict_observation

    def render(self):
        pass

    def close(self):
        pass

    def add_sample(self, sample: Sample, weight: int = 1.0):
        self.sampler_for_replaying.add(sample, weight)
